The DOI matches its Crossref record; 71 of the 88 references it cites were found in a registry or an index; 8 of the 27 sources its claims cite were checked for support.
Vision Transformers for Dense Prediction
Open accessAbstract
We introduce dense prediction transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense prediction transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense prediction transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at https://github.com/intel-isl/DPT.
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32 Own result · 23 Own interpretation · 11 No basis given · 10 No full text of the sources · 3 Supported · 2 Partly supported · 1 Supported so far · 1 Own method · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Specifically, we use the recently proposed vision transformer (ViT) [11] as a backbone architecture.”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
Section 3.1 (p. 3) introduces and describes the Vision Transformer (ViT) architecture. The citation identifies the ViT used as the citing authors’ backbone; their choice to use it is not a claim attributed to this source. (LLM, conf=0.93)
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In the paper
“Virtually all existing architectures for dense prediction are based on convolutional networks [6,31,34,42,49,50,53].”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Partly supportedcites [6] 6. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected crfs. TPAMI, 40(4):834-848, 2018. sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says convolutional models such as PixelCNN had received much more attention for pixel-by-pixel image generation. It does not establish that virtually all architectures across the broader category of dense prediction are convolutional; it also discusses recurrent models and proposes a self-attention alternative (Introduction, p. 2). (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [31] 31. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- Net: Convolutional networks for biomedical image segmen- tation. In MICCAI, 2015. source
- Supportedcites [34] 34. Evan Shelhamer, Jonathan Long, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CVPR, 2015. source
The paper is an example of a convolutional-network architecture for dense prediction: its Abstract (p. 1) describes predicting a depth map, and §3.1.2 (p. 3) identifies its convolutional layers and target-depth output. As one of several citations, it supports the sentence by exemplifying such an architecture, rather than establishing the prevalence claim on its own. (LLM, conf=0.88)
- Could not verifycites [42] 42. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao. Deep high-resolution representation learning for visual recogni- tion. TPAMI, 2020. sourceExperimental — accuracy not yet measured
Section 2.1 (p. 8) describes transformer language-model architectures, not architectures for dense prediction. Searches for dense prediction, convolutional networks, and pixel-wise segmentation found no passage addressing whether dense-prediction architectures are predominantly convolutional. (LLM, conf=0.78)
- Not matchedcites [49] 49. Fisher Yu and Vladlen Koltun. Multi-scale context aggrega- tion by dilated convolutions. In ICLR, 2016.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [50] 50. Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object- contextual representations for semantic segmentation. In ECCV, 2020. source
- Not checked — no full text of the sourcecites [53] 53. Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. source
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In the paper
“We notice that the biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations [15,55,59].”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [15] 15. Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. source
- Supportedcites [55] 55. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012. source
The citation identifies MPI-Sintel as one of the datasets in the citing authors’ comparison; the performance gains are the citing authors’ observation, not a result attributed to this paper. Design Decisions and Comparison to Middlebury (p. 5) describes Sintel’s higher-resolution frames, and The Sintel Data Set (p. 9) describes its flow fields and pixel-level error evaluation. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [59] 59. Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and An- dreas Geiger. A multi-view stereo benchmark with high- resolution images and multi-camera videos. In CVPR, 2017. source
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In the paper
“Note that ResNext101-WSL was pretrained on a billion-scale corpus of weakly supervised data [25] in addition to ImageNet pretraining.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Partly supportedcites [25] 25. Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten. Exploring the limits of weakly supervised pretraining. In ECCV, 2018. sourceExperimental — accuracy not yet measured
Section 2.3 (p. 4) identifies the models as ResNeXt-101, and Section A.5 (p. 23) says they were pretrained on billions of Instagram images using hashtags as labels. But it describes subsequent training on ImageNet as fine-tuning, not additional ImageNet pretraining; Section 3.2 (p. 12) also distinguishes Instagram-pretrained models from ImageNet-pretrained baselines. (LLM, conf=0.88)
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In the paper
“While ignoring the token yields good performance, projection provides slightly better performance on average.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Adding the token, on the other hand, yields worse performance than simply ignoring it.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Both DPT variants significantly outperform the state of the art.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“Note that both architectures have similar latency to MiDaS (Table 9).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“ViT-Large outperforms all other backbones but is also almost three times larger than ViT-Base and ViT-Hybrid.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“We again see that DPT can provide strong performance even on smaller datasets.”
§ Vision Transformers for Dense Prediction > ADE20K.
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- Probably not a substantial claim. 11
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Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, it has recently been demonstrated that a direct application of token-based transformer architectures that have been successful in NLP can yield competitive performance on image classification [11].”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says image patches are treated like NLP tokens and fed to a standard Transformer. The Conclusion (p. 9) reports that this approach matches or exceeds the state of the art on many image-classification datasets when coupled with pre-training on large datasets. (LLM, conf=0.93)
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In the paper
“A key insight of this work was that, like transformer models in NLP, vision transformers need to be paired with a sufficient amount of training data to realize their potential.”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says ViT performs modestly on mid-sized datasets and does not generalize well with insufficient data, but attains excellent results when pre-trained at sufficient scale. Section 4.3 (p. 6) reports that larger ViT models show their full benefit only with the largest pre-training dataset tested. The Conclusion (p. 9) also describes the approach as working well when coupled with pre-training on large datasets. (LLM, conf=0.94)
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In the paper
“Similarly, the ViT-Hybrid architecture extracts features at 1 16 the input resolution, which is twice as high as the lowest-resolution features that are commonly used with convolutional backbones.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“It contains about 1.4 million images and is, to the best of our knowledge, the largest training set for monocular depth estimation that has ever been compiled.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“We observe that the DPT tends to produce cleaner and finer-grained delineations of object boundaries and that the predictions are also in some cases less cluttered.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DPT can better reconstruct fine details while also improving global coherence in areas that are challenging for the convolutional architecture (for example, large homogeneous regions or relative depth arrangement across the image).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“The average relative improvement over the best published architecture, MiDaS, is more than 23% for DPT-Hybrid and 28% for DPT-Large.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“DPT-Hybrid achieves this with a comparable network capacity (Table 9), while DPT-Large is about 3 times larger than MiDaS.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“We observe the tendency that attention is spatially more localized close to the reference token in shallow layers (leftmost columns), whereas deeper layers (rightmost columns) frequently attend across the whole image.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“ViT-Base has comparable performance to ResNext101-WSL, while ViT-Hybrid and ViT-Large improve performance even though they have been pretrained on significantly less data.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“We observe more details and also better global depth arrangement in DPT predictions when compared to the fullyconvolutional baseline.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“As long as the image size is divisible by p, the embedding procedure can be applied and will produce a varying number of image tokens N p .”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“Unlike fully-convolutional networks, the vision transformer backbone foregoes explicit downsampling operations after an initial image embedding has been computed and maintains a representation with constant dimensionality throughout all processing stages.”
§ Vision Transformers for Dense Prediction > 1. Introduction
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In the paper
“Since transformers are known to realize their full potential only when an abundance of training data is available, monocular depth estimation is an ideal task to test the capabilities of DPT.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“This is in stark contrast to convolutional networks, which progressively increase their receptive field as features pass through consecutive convolution and downsampling layers.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“DPT-Large performs slightly worse, likely because of the significantly smaller dataset compared to our previous experiments.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DeIT trains the ViT architecture with a more data-efficient pretraining procedure.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Note that the DeIT-Base architecture is identical to ViT-Base, while DeIT-Base-Dist introduces an additional distillation token, which we ignore in the Reassemble operation.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Similar to prior work on transformers, DPT unfolds its full potential when trained on large-scale datasets.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
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In the paper
“Note that results for DPT and Mi-DaS are computed at the same input resolution (384 pixels).”
§ Vision Transformers for Dense Prediction > Additional information
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The choice of backbone architecture has a large influence on the capabilities of the overall model, because information lost in the encoder cannot be recovered in the decoder.
“However, it is widely recognized that the choice of backbone architecture has a large influence on the capabilities of the overall model, as any information that is lost in the encoder is impossible to recover in the decoder.”
The paper relies on this without support.
Feature resolution and granularity are critical for dense prediction, where the architecture should ideally resolve features at or close to the input resolution.
“While feature resolution and granularity may not matter for some tasks, such as image classification, they are critical for dense prediction, where the architecture should ideally be able to resolve features at or close to the resolution of the input image.”
The paper relies on this without support.
Convolutions are linear operators with a limited receptive field.
“Convolutions, by definition, are linear operators that have a limited receptive field.”
The paper relies on this without support.
Downsampling intermediate representations is necessary to keep memory consumption at levels feasible with existing computer architectures.
“Downsampling the intermediate representations is necessary to keep memory consumption at levels that are feasible with existing computer architectures.”
The paper relies on this without support.
The transformer backbone maintains a constant feature resolution.
“Since the transformer backbone maintains a constant feature resolution, it is not clear at which points in the backbone features should be tapped.”
The paper relies on this without support.
Performance of fully-convolutional architectures suffers heavily when performing inference at an input resolution significantly different from the training resolution.
“Performance thus suffers heavily when performing inference at an input resolution that is significantly different from the training resolution.”
The paper relies on this without support.
Transformer encoders have a global receptive field in every layer.
“Transformer encoders, on the other hand, have a global receptive field in every layer.”
The paper relies on this without support.
The reduced meta-dataset datasets provide high-quality ground truth.
“We choose these datasets since they provide high-quality ground truth.”
The paper relies on this without support.
Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.
“Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.”
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In the citing paper
“Vision transformers, which are a deep learning approach to encoding low-dimensional input into a high dimensional feature space, have established new frontiers in depth estimation compared to convolutional neural networks (Ranftl et al., 2021).”
From Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2024) · cites this paper as Ranftl et al., 2021
In the citing paper
“However, the Transformer framework does not involve dimensionality reduction10,12, thus keeping all attention layer traceable to the original input features13, thus making the models interpretable.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
In the citing paper
“For example, the change of dimensions and non-linear aggregation of features throughout the autoencoder’s deep processing stages leads to untraceable and uninterpretable latent space and loss of information and feature resolution9,10.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
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In the paper
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
“Our architecture matches or improves state-of-the-art performance on both datasets in all metrics.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
“| BTS [20] | 0.956 | 0.993 | 0.998 | 0.059 2.756 | 0.096 | | DPT-Hybrid 0.959 | 0.995 | 0.999 | 0.062 2.573 | 0.092 |”
Citations
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BackgroundAdam: A Method for Stochastic Optimization
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InfluentialBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BackgroundMethodInfluentialAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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BackgroundLanguage Models are Few-Shot Learners
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Cited by
Sorted by citations
Items 1,401–1,500 of 2,248
FlowDreamer: A RGB-D World Model With Flow-Based Motion Representations for Robot Manipulation
Guo, J., Ma, X., Wang, Y., Yang, M., Liu, H., & Li, Q. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3653273 · Cited by 2
“To autoregressively imagine the future, we leverage a pre-trained depth estimation model [56] with a simple DPT [57] head to predict the metric depth D t +1 from I t +1 .”
Akers, J., Buck, A. R., Anderson, D. T., Keller, J. M., Camaioni, R., Deardorff, M., & Luke, R. (2023) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr60534.2023.10440660 · Cited by 1
Beyond-Labels: Advancing Open-Vocabulary Segmentation with Vision-Language Models
Rahman, M. M., Choi, D., Lee, S., & Min, K.-W. (2025) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci65340.2025.11096230 · Cited by 1
Long Staple Cotton Detection Method Based on improved Swin Transformer model
Li, X., Cui, G., & Lin, T. (2023) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac59482.2023.10401423 · Cited by 1
Utilizando a Arquitetura UNet++ na Estimativa de Profundidade Monocular
Guzzo, L. A. R., & Gazolli, K. A. D. S. (2023) · Anais do Seminário Integrado de Software e Hardware (SEMISH) · doi:10.5753/semish.2023.229972 · Cited by 1
“Recentemente, a arquitetura Transformer [Vaswani et al. 2017] tem sido aplicada nessa tarefa [Agarwal and Arora 2022, Bhat et al. 2022, Ranftl et al. 2021], levando a um aumento da acurácia, bem como, dos custos computacionais e do tempo de latência.”
BackgroundDINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integration
Oh, Y., Kwon, J., & Cho, N. I. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31335-5_8 · Cited by 1
Monocular Depth Estimation with Boundary Attention Mechanism and Shifted Window Adaptive BinsPreprint
Liang, M., Wang, C., Zhao, M., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768659 · Cited by 1
Wu, C., Peng, D., Lu, Y., Zou, Y., Liu, Z., Onoda, H., Washizaki, H., Kameyama, W., & Liu, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3647465 · Cited by 1
Sun, P., Jiang, J., Yao, Y., Chen, Y., Zhao, W., Jiang, K., & Liu, X. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245979 · Cited by 2
Nam, W., Lee, S., Lee, J., Choe, H., Ha, S., & Lee, K. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2024.3523891 · Cited by 2
“To further validate the generalizability of N-epitomizer across various network architectures, we employ DPT [48], which includes multiple self-attention layers for depth estimation, and SAT [49], whose decoder consists of long short-term memory (LSTM) cells for image captioning.”
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Lajoie, P., Ramtoula, B., Martini, D. D., & Beltrame, G. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3609204 · Cited by 1
“Recent advancements in monocular depth estimation – such as DPT [13] – offer promising alternatives for scale estimation.”
A Novel Spatial Attention Module (SAM) for Alzheimer's Detection from MRI Images
Roy, S., Gupta, A., Tiwari, S., et al. (2024) · doi:10.1145/3702250.3702274 · Cited by 1
“Vision Transformer [28] which is an equivalent model of transformer [43], utilized widely in NLP, still is not an automatic choice for researchers in the domain of computer vision.”
LDIP: Real-time on-road object detection with depth estimation from a single image
Xu, C., Sun, X., Xu, Y., & Wang, R. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801572 · Cited by 1
“Broadly, these methods can be classified into two categories: those treating depth estimation as a pixel-wise regression problem [13]–[15] and those treating it as a pixel-wise classification problem [16], [17].”
VIT-DepthSeg: unified panoptic segmentation and depth estimation using vision transformer
Yu, S., Tang, F., Zhao, Y., et al. (2025) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3067342 · Cited by 1
Pedestrian Action Classification from a Vehicle's Perspective
Plaza, J. V., de la Escalera, A., & Armingol, J. M. (2024) · doi:10.1109/robot61475.2024.10797373 · Cited by 1
“Another important task to address is improving the temporal and spatial consistency of the depth maps generated by MiDaS [4], [5].”
InfluentialMonocular depth estimation for 2D mapping of simulated environments
Barhoum, M., & Pyrkin, A. A. (2024) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2024-24-1-118-123 · Cited by 1
HCTNet: Hybrid CNN-Transformer Architecture Network for Self-Supervised Monocular Depth Estimation
Ma, L., Fu, Y., Lu, X., et al. (2023) · doi:10.1109/csat61646.2023.00098 · Cited by 1
Controllable 3D Object Generation with Single Image Prompt
Lee, J., & Lee, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78172-8_15 · Cited by 1
“Given the input image x masked with M , which is acquired by a dense prediction transformer [30], and the predicted image ˆ x r masked with M r , we perform the reconstruction by comparing x and ˆ x r .”
Mohrat, M., Berkaev, A., Burkov, A. M., & Kolyubin, S. A. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342329 · Cited by 1
“We process RGB key frames from the available cameras in DPT [6] and LeReS [5] as discussed in Section III.”
MethodPT-MVSNet: Overlapping Attention Multi-view Stereo Network with Transformers
Zhang, S., Li, L., & Qiao, J. (2023) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl58838.2023.10167367 · Cited by 1
“In the current research[7], the research in the field of 3D reconstruction can be divided into two main categories, traditional multi-view geometry-based algorithms, and deep learning-based algorithms.”
MethodBack on Track: Bundle Adjustment for Dynamic Scene Reconstruction
Chen, W., Zhang, G., Wimbauer, F., Wang, R., Araslanov, N., Vedaldi, A., & Cremers, D. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00471 · Cited by 1
“Regression-based approaches leverage large-scale training to estimate depth from single RGB images [3, 20, 32, 35, 52], while video-based extensions further improve temporal coherence by incorporating multi-frame cues, depth consistency, or diffusion priors [14, 17, 25, 28].”
OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots
Atar, S., Li, Y., Grotz, M., Wolf, M., Fox, D., & Smith, J. R. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245882 · Cited by 1
“The output from the DPT [5] model is then combined with the segmentation mask obtained from STOW [40], provides the segmentation masks, and tracks unseen object instances in discrete frames.”
InfluentialHan, M., Luo, Y., Hu, H., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02545-w · Cited by 1
3D Deformable Object Matching Using Graph Neural Networks
Loghin, M.-A. (2024) · Studia Universitatis Babeș-Bolyai Informatica · doi:10.24193/subbi.2024.1.02 · Cited by 1
Xia, C., Zhang, A., Gao, X., Ge, B., Li, K., Fang, X., Liang, X., & Zhang, Y. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105434 · Cited by 1
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“Nonetheless, these methods still encounter challenges in achieving high-accuracy camera poses.”
DBWaterNet: Dual-branch joint refinement for underwater image enhancement
Ibrahim, M., Wang, J., Shuaibu, Z., et al. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104847 · Cited by 1
Detection of the 3D Ground Plane from 2D Images for Distance Measurement to the Ground
Çakıroğlu, O., Wieser, V., Zellinger, W., et al. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-14343-4_5 · Cited by 1
Residual Spatial Reduced Transformer Based on YOLOv5 for UAV Images Object Detection
Chen, L., Cang, N., Zhang, W., Zhang, C., Zhang, W., & Guo, D. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424500071 · Cited by 1
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“Current research generally adopts Monocular Depth Esti - mation (MDE) methods to generate depth data, such as Wang et al. [ 11 ] using NewCRFs [ 17 ], Wu et al. [6] using DPT [ 18 ], and Xiang et al. [ 12 ] and Liu et al. [ 7 ] using MiDas [ 19 ].”
Zhang, M., Li, S., Wang, D., et al. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.110014 · Cited by 1
Rethinking Attention Module Design for Point Cloud Analysis
Wu, C., Wang, K., Zhong, Z., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78395-1_17 · Cited by 1
HFP-SAM: Hierarchical Frequency Prompted SAM for Efficient Marine Animal Segmentation
Zhang, P., Yan, T., Wang, Y., Liu, Y., Tang, T., Ma, Y., Lv, L., Tian, F., Sun, W., & Lu, H. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3674678 · Cited by 1
“Recently, Vision Transformer [8] demonstrates great potential in extracting long-range dependencies and global contexts, resulting in significant improvements for various image segmentation tasks [26], [27], [28].”
Swaminathan, V., Thomasson, J. A., Rajan, N., et al. (2025) · Remote Sensing · doi:10.3390/rs17040579 · Cited by 1
User-in-the-Loop View Sampling with Error Peaking Visualization
Yasunaga, A., Saitô, H., & Mori, S. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084757 · Cited by 1
A dual-branch RGB-T salient object detection via spatial-frequency integration
Yu, X., Huang, J., Wang, Y., & Chen, J. (2026) · The Visual Computer · doi:10.1007/s00371-026-04359-4 · Cited by 1
Song, J., & Lee, S. J. (2025) · Sensors · doi:10.3390/s25175366 · Cited by 1
“The depth models adopt the architecture of a depth foundation model based on DPT [17], utilizing a ViT-based encoder [23] to extract rich visual representations.”
UMPAN: Multipriors Aggregation Network for Single Underwater Image Enhancement
Wang, Z., Shen, L., Zhu, Q., et al. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3577515 · Cited by 1
“Based on the verification results in [57], DPT [58] produces the highest quality depth maps in current underwater image scene depth estimation networks.”
Polar Subarea-Aware Fusion Net for Posterior Eyeball Shape Reconstruction
Zhang, J., Wu, X., Liu, J., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3642381 · Cited by 1
“This stage is constructed by distilling the { 3 , 6 , 9 , 12 } -th layers from a pre-trained ViT-Base model [24], an e ff ective strategy for adapting large models to smaller datasets proved by [31], [32], and [33].”
Multiscale object detection model based on pyramid vision transformer
Baek, J.-W., Suh, D., & Chung, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-43522-8 · Cited by 2
ADfM-Net: An Adversarial Depth-From-Motion Network Based on Cross Attention and Motion Enhanced
Long, Y., Yu, H., Xu, C., & Deng, Z. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3268885 · Cited by 1
“In depth estimation [27], [28] introduce the transformer architecture achieving unprecedented performance.”
BackgroundChen, J., He, Z., Wu, F., Zhao, Z., & Chen, X. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112310 · Cited by 3
Gąsienica-Józkowy, J., Cyganek, B., Knapik, M., Głogowski, S., & Przebinda, Ł. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f3560 · Cited by 1
“3) Monocular depth estimation: In this step, we utilize the monocular depth estimation neural network called DPT Levit 224 [2], [3].”
BackgroundMethodEdge Attention Module for Object Classification
Roy, S., Suresh, A., Adhikari, P., et al. (2025) · doi:10.1109/ijcnn64981.2025.11227631 · Cited by 1
“In recent years, significant progress has been made in the field of object classification tasks, driven by advancements in Convolutional Neural Networks (CNNs) and emerging technologies such as Vision Transformer (ViT) [1].”
Atlantis++: Enabling Underwater Depth Estimation with Stable Diffusion and Beyond
Zhang, F., You, S., Li, Y., & Fu, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02823-1 · Cited by 1
“Remarkably, MiDaS family (Ranftl et al., 2020, 2021; Bhat et al., 2023) set a new benchmark for robust zero-shot depth estimation by training on million-scale multi-source data with various optimization techniques.”
Abdallah, B., & Raza, S. E. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98691-8_17 · Cited by 1
ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
Quero, C. A. O., Durini, D., & Martínez-Carranza, J. (2025) · Applied Sciences · doi:10.3390/app15116138 · Cited by 1
Yang, Y., Li, L., Wang, L., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108313 · Cited by 1
Won, R.-B., Choi, M., Choi, J. H., & Bae, B. (2024) · Journal of Broadcast Engineering · doi:10.5909/jbe.2024.29.5.662 · Cited by 1
“Creating a 3D scene starts by producing detailed depth maps from a single image to approximate the scene's geometry using the dense prediction transformer (DPT) [8] .”
BackgroundDeep-DSO: Improving Mapping of Direct Sparse Odometry Using CNN-Based Single-Image Depth Estimation
Herrera-Granda, E. P., Torres, J. C., Herrera-Granda, I. D., Lucio-Naranjo, J., Rosales, A., Revelo-Fuelagán, J., & Peluffo-Ordóñez, D. H. (2025) · Mathematics · doi:10.3390/math13203330 · Cited by 1
Yang, S., Wu, X., Zhou, Y., jia, H., Bao, W., Rong, W., Wang, Q., Zou, T., & Xue, X. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112443 · Cited by 1
Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction
Pannone, D., Castronovo, A., Mancini, M., Foresti, G. L., Piciarelli, C., Gabrieli, R., Bilal, M., & Avola, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-10192-1_23 · Cited by 1
Joint Modeling of Pixel-Wise Visibility and Fog Structure for Real-World Scene Understanding
Wu, J., Li, J., Wang, J., et al. (2025) · Atmosphere · doi:10.3390/atmos16101161 · Cited by 1
Aesthetics Without SemanticsPreprint
Párraga, C. A., Gonzalez, M. M., Penacchio, O., Raducanu, B., & Otazu, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4817083 · Cited by 1
Automated evaluation of game content display using deep learning
Păduraru, C., Cernat, M., & Ştefănescu, A. (2024) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3661167.3661184 · Cited by 1
Tan, X., Wang, C., Zhang, Z., Ping, Z., Pan, J., Shan, H., Li, R., Chi, M., & Cui, Z. (2025) · Sustainability · doi:10.3390/su172411271 · Cited by 1
Object-Aware Monocular Depth Prediction With Instance Convolutions
Simsar, E., Örnek, E. P., Manhardt, F., Dhamo, H., Navab, N., & Tombari, F. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3155823 · Cited by 2
“[30] proposed attention blocks within the decoder, while other methods adopt a fully Transformer based architecture [31], [32].”
MethodInteractive Editing of Monocular Depth
Dukor, O. S., Miangoleh, S. M. H., Reddy, M. K. K., et al. (2022) · ACM SIGGRAPH Posters · doi:10.1145/3532719.3543235 · Cited by 1
“Currently, state-of-the-art approaches [Miangoleh et al. 2021; Ranftl et al. 2021] can generate high-resolution depth estimations with high edge accuracy that enables convincing photography applications.”
BackgroundEvSAM: Segment Anything Model with Event-based Assistance
Ding, Y., Yao, B., Liu, Y., et al. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3786794 · Cited by 1
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation
Niu, Q., Zhou, Y., Chen, S., Zhang, T., & Ji, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00406 · Cited by 1
“Subsequent architectural innovation [38] uses Vision Transformer (ViT) [14] to replace convolutional networks in depth prediction tasks.”
Monocular Depth Estimation for 3D Map Construction at Underground Parking Structures
Li, J., Song, X., Gao, R., & Tao, D. (2023) · Electronics · doi:10.3390/electronics12112390 · Cited by 1
Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM
Habib, Y., Papadakis, P., Barz, C. L., Fagette, A., Gonçalves, T., & Buche, C. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2415084 · Cited by 2
“Recently, methods such as DPT [23], AdaBins [24], or ZoeDepth [25] have integrated Vision Transformers to maintain high resolution throughout the DNN and predict more detailed depth maps.”
Rethink the Application of Pyramid Structure Transformer Networks in Semantic Segmentation
Xu, Q., Wang, H., & Chen, F. (2025) · doi:10.1145/3766918.3766967 · Cited by 1
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding
Xia, J., Cao, G., Ma, G., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01486-3_5 · Cited by 1
Zhang, H., Yu, Z., Jin, Y., Liu, X., Sheng, W., Liu, R., Li, X., Liu, Q., & Cheng, R. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3614261 · Cited by 2
“On the KITTI dataset, our method achieves an Abs Rel of 0.061 and a δ 1 accuracy of 0.957, which are competitive with large-scale models such as DPT [33], while using less than 5 percent of the parameters.”
InfluentialLightweight monocular absolute depth estimation based on attention mechanism
Jin, J., Tao, B., Qian, X., Hu, J., & Li, G. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.2.023010 · Cited by 1
“Varma et al. 11 performed a self-supervised monocular depth estimation of in-camera reference values using a dense prediction converter 20 and incorporated a prediction head to estimate the in-camera parameter values.”
MethodHuang, Z., Islam, M., Armstrong, B. J., et al. (2026) · Science of Remote Sensing · doi:10.1016/j.srs.2026.100372 · Cited by 1
“DPT decoder (Ranftl et al., 2021) for ViT backbones (e.g., ViT-S / 16).”
SARFormer - An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data
Prexl, J., Recla, M., & Schmitt, M. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00209 · Cited by 4
“From here we can directly follow [19] and use features gathered from different layers and processed on different resolution levels which results in N conv feature maps f convi (compare supplementary materials for details).”
InfluentialSelf-supervised vision transformers for semantic segmentation
Gu, X., Hu, Y., Wen, C., & Gao, Y. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104272 · Cited by 2
Towards Explainability in Monocular Depth Estimation
Arampatzakis, V., Pavlidis, G. P., Pantoglou, K., et al. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74627-7_34 · Cited by 1
Boosting few-shot learning via selective patch embedding by comprehensive sample analysis
Yang, J., Zhang, Y., Wang, R., & Xue, L. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01669-w · Cited by 1
Fusing Transformer and FCN for Polyp Segmentation
Li, W., Liu, Y., Liu, X., et al. (2023) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait60137.2023.10528540 · Cited by 1
A Geography of indoors for analyzing global ways of living using computer visionPreprint
Mazzarello, M., Klimenka, M., Sanatani, R. P., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4676415/v1 · Cited by 1
CAgMLP: An MLP-like architecture with a Cross-Axis gated token mixer for image classification
Jiang, J., Zhang, Q., Cui, Y., et al. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104590 · Cited by 1
Continual learning of conjugated visual representations through higher-order motion flows
Marullo, S., Tiezzi, M., Gori, M., & Melacci, S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107296 · Cited by 1
Regularizing Sparse Input 3D Gaussian Splatting with Geometry Priors
Li, W., Zhang, Z., Ye, Z., et al. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6972-1_9 · Cited by 1
ResNet50-based Automated Method for Monocular Depth Estimation using Video
Kouloorkar, S., Gurjar, K., Ray, K. P., et al. (2024) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct62155.2024.10677064 · Cited by 1
“To obtain ground truth depth images for dataset, the MiDaS model developed by Intel is implemented [15], [16].”
MethodHand Gesture-Guided Robotic Manipulator for Handling Laparoscope During Minimally Invasive Surgery
Suksawasdi, Y., Oranrigsupak, T., Arjanurak, N., et al. (2025) · doi:10.23919/sicefes67750.2025.11236549 · Cited by 1
“Initially using MiDaS by Ranftl et al. [18] [19], we observed unstable depth predictions at short range.”
Spatiotemporally Consistent Indoor Lighting Estimation with Diffusion Priors
Tong, M., Wu, R., & Zheng, C. (2025) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3721238.3730749 · Cited by 1
Guo, X., Zhao, H., Shao, S., Li, X., Zhang, B., & Li, N. (2024) · Remote Sensing · doi:10.3390/rs16122221 · Cited by 1
Desman, A., Muchtar, K., Oktiana, M., et al. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930019 · Cited by 1
Adaptive-Fusion of Geometric and Semantic Information for Floorplan Localization
Zhong, Y., Peng, S., Ou, Y., Cai, Y., & Yu, H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3637941 · Cited by 2
“Monocular depth estimation methods based on deep learning [19], [20], [21] can predict dense depth maps from a single image but face the problem of scale ambiguity.”
Visual Odometer Based on Image Region Texture Weights of ORB FeaturesPreprint
Wu, D., Ma, Z., Xu, W., He, H., & Li, Z. (2023) · Preprints.org · doi:10.20944/preprints202307.1477.v1 · Cited by 1
A Fundus-Based Color Mapping Method to Optimize RNFL Clock Hours for Glaucoma Screening
Sangchocanonta, S., Patchimnan, K., Phienphanich, P., Munthuli, A., Puangarom, S., Itthipanichpong, R., Ratanawongphaibul, K., Chansangpetch, S., Manassakorn, A., Tantisevi, V., et al. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252997 · Cited by 1
“Building upon this foundation, our upcoming research presented at EMBC 2025 will leverage these insights to enhance screening methodologies by utilizing the DPT-Large transformer model [15], thereby improving patient outcomes through more precise identification of at-risk individuals.”
Masked Channel Modeling Enables Vision Transformers to Learn Better Semantics
Chen, J., Ma, Y., Dai, W., & Li, Z. (2025) · Entropy · doi:10.3390/e27080794 · Cited by 1
“For general Vision Transformers, the features extracted from a specific spatial patch of an image should reflect a certain level of spatial continuity [4,6].”
Distortion-Aware Depth Self-Updating for Self-Supervised Fisheye Monocular Depth Estimation
Xu, Y., & Dong, Q. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3661813 · Cited by 2
“This superior performance is primarily attributed to the integration of Vision Transformers, which have proven e ff ective in various visual tasks [61], [62], [63].”
Performing Defocus Deblurring by Modeling its Formation Process
Zhang, Z., Foo, L. G., Rahmani, H., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00548 · Cited by 1
“These depth maps are estimated using DPT [1], and it can be observed that the quality of the estimated depth maps is high.”
Zhou, H., Tuo, H., Jing, Z., et al. (2025) · Aerospace Systems · doi:10.1007/s42401-025-00415-4 · Cited by 1
SPIN-SGG: spatial integration for open-vocabulary scene graph generation
Liang, N., Yang, X., Wang, S., Liu, Y., & Xia, Y. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00203-2 · Cited by 1
“Given an input image I ∈ R H × W × 3 , we first obtain a depth map D ∈ R H × W using a pretrained monocular depth estimator F d : (5) We adopt a high-resolution, generalizable depth model such as DPT (Ranftl et al. 2021) or Depth-Anything (Chen et al. 2023), which performs well across diverse domains.”
InfluentialLiu, Y., Wang, Q., & Tan, C. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02274-1 · Cited by 1
“In addition, the physical limitations of these sensors, such as measurement noise under adverse weather conditions or on highly reflective surfaces, can distort the super - vision signals [11, 12].”
Robust Environmental Perception of Semantic Segmentation
Pan, H., Wang, J., Yu, X., Sun, W., & Gao, H. (2023) · Recent advancements in connected autonomous vehicle technologies · doi:10.1007/978-981-99-7790-1_4 · Cited by 1
Ai, B., Wu, Z., & Hsu, D. (2024) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-63596-0_7 · Cited by 1
A Simple Approach Towards Frame Filtering for Efficient Gaussian Splatting
Tran, T.-P., Nguyen, M.-Q., & Tran, M. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4282-3_19 · Cited by 1
Wang, S., Yang, J., Chen, B., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3628135 · Cited by 1
“Table V compares the e ffi ciency of FAC and DTL [31], E2Depth [15], DPT [44], EReFormer [19], using the parameter size and Floating-Point Operations (GFLOPs).”
Real-Time Approach to Neural Network-Based Disparity Map Generation from Stereo Images
Kasatkin, N., & Yudin, D. A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-91581-0_35 · Cited by 1
Feature‐enhanced representation with transformers for multi‐view stereo
Xiang, L., & Yin, H. (2024) · IET Image Processing · doi:10.1049/ipr2.13046 · Cited by 1
Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering
Nakamura, K., Nozawa, Y., Lin, Y.-C., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0885-0_16 · Cited by 1
Metadata
- Authors
- Ranftl, R., Bochkovskiy, A., & Koltun, V.
- Affiliations
- Intel Labs
- Intel (United States) (US)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12159-12168
- OpenAlex
- W3136635488
- Cited by
- 2,373
- References
- 88
- Citations by year
- 2026: 480 · 2025: 607 · 2024: 586 · 2023: 431 · 2022: 225 · 2021: 44
- FWCI
- 104.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- vision transformer, dense prediction, monocular depth estimation, semantic segmentation, convolutional decoder, global receptive field, Pascal Context, NYUv2, KITTI, ADE20K
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